Clinical impact and cost-effectiveness of updated 2023/24 COVID-19 mRNA vaccination in high-risk populations in the United States
Notice bibliographique
Résumé
Abstract Introduction In the post-pandemic era, people with underlying medical conditions continue to be at increased risk for severe COVID-19 disease, yet COVID-19 vaccination uptake remains low. This study estimated the clinical and economic impact of updated 2023/24 Moderna COVID-19 vaccination among high-risk adults versus no updated vaccination and versus updated Pfizer/BioNTech vaccination. Methods A static Markov model was adapted for high-risk adults, including immunocompromised (IC), chronic lung disease (CLD), chronic kidney disease (CKD), cardiovascular disease (CVD), and diabetes mellitus (DM) populations in the United States. Results Vaccination with the updated Moderna vaccine at current coverage rates was estimated to prevent considerable COVID-19 hospitalizations in CLD (101,309), DM (97,358), CVD (47,830), IC (14,834) and CKD (13,558) populations versus no updated vaccination. Vaccination also provided net medical cost savings of $399M–2,129M (healthcare payer) and $457M–2,531M (societal perspective), depending on population. The return-on-investment was positive across all conditions ($1.10–$2.60 gain for every $1 invested). Healthcare savings increased with a relative 10% increase in current vaccination coverage ($439M–$2,342M), and from meeting US 2030 targets of 70% coverage ($1,096M–$5,707M). Based on higher vaccine effectiveness observed in real-world evidence studies, updated Moderna vaccination was estimated to prevent additional COVID-19 hospitalizations in DM (13,105), CLD (10,359), CVD (6,241), IC (1,979), and CKD (942) versus Pfizer/BioNTech’s updated vaccine, with healthcare payer and societal cost savings, making it the dominant strategy. Healthcare savings per patient vaccinated with Moderna versus Pfizer/BioNTech’s updated vaccine were $31-59, depending on population. Results were robust across sensitivity/scenario analyses. Conclusions Updated 2023/24 Moderna COVID-19 vaccination was estimated to provide significant health benefits through prevention of COVID-19 in high-risk populations, and cost-savings to healthcare payers and society, versus no vaccination and updated Pfizer/BioNTech vaccination. Increasing current low COVID-19 vaccination coverage rates was estimated to be cost-saving while preventing many more severe infections and hospitalizations in these high-risk populations. Key Summary Points Why carry out this study? In the US, people with underlying medical conditions continue to be at high risk of severe COVID-19, yet vaccination rates are low. The CDC recommends an updated 2024/25 COVID-19 vaccination for everyone aged >6 months. The objective of this study was to estimate the clinical benefits and cost-effectiveness of updated 2023/24 Moderna COVID-19 vaccination in people with high-risk conditions, versus no updated vaccination, and versus updated Pfizer/BioNTech COVID-19 vaccination. What was learned from the study? COVID-19 vaccination with the updated Moderna mRNA vaccine of people with underlying medical conditions at high-risk of severe COVID-19 was cost-saving versus no updated vaccination. It also provided more health gains with cost savings versus Pfizer/BioNTech, making it the dominant strategy. For every $1 spent on vaccination, the updated Moderna vaccination provided a return-on-investment of $1.10–$2.60 versus no updated vaccination, depending on the high-risk population. Healthcare cost savings were $31-59 per patient vaccinated with Moderna’s versus Pfizer/BioNTech’s updated vaccination, depending on the high-risk population. A relative 10% increase in vaccination coverage rates prevented 10% more hospitalizations and deaths, and increased healthcare and societal cost savings in all high-risk populations, with the potential for significant health and financial benefits with greater vaccine coverage rates.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».